In this analysis, psychosis spectrum (PS) status is predicted based on 22q11.2 deletion syndrome (22q) characteristics.

0. Sample sizes

Sample sizes for face2gene scores and the original prediction task:

22q PS TD
150 55 93

Total sample sizes in 03/08/22 data (first row) and non-missing data per variable:

22q PS TD total
150 55 93 298
height 50 19 14 83
weight 110 36 29 175
GAF_C 128 29 23 180
GAF_H 125 29 23 177
MMSE 111 41 41 193
VIQ 76 0 0 76
PIQ 76 0 0 76
FSIQ 76 0 0 76
DSM Dx 150 0 0 150
PS 148 0 0 148

Note: Emotrics available for all records.

1. Examining characteristics of 22q sample with PCA

PCA was performed without scaling on the 22q sample alone.

Scree Plot

Using the “elbow rule”, subsequent analysis will focus on the first 4 PCs.

Loadings

Loadings for the first 2 PCs are plotted below.

PC1

PC2

Scores and effects

## Analysis of Variance Table
## 
## Response: PC2
##            Df Sum Sq Mean Sq F value   Pr(>F)   
## sex         1 0.2870 0.28702  4.9192 0.028207 * 
## age         1 0.6551 0.65507 11.2270 0.001042 **
## race        4 0.3486 0.08715  1.4935 0.207518   
## Residuals 137 7.9937 0.05835                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Sex

Age

## Warning: Removed 6 rows containing non-finite values (stat_smooth).
## Warning: Removed 6 rows containing missing values (geom_point).

Emotrics

PC2 scores were correlated with Brow Height (Left and Right), Marginal Reflex Distance 2 (Left and Right), and Philtrum measurements.

PC1 p PC2 p
Brow_Height_Right -0.1837632 0.0243851 -0.2986967 0.0002050
Brow_Height_Left -0.1143783 0.1634075 -0.3526283 0.0000096
Marginal_Reflex_Distance_1_Right 0.0314815 0.7021367 -0.1502111 0.0665447
Marginal_Reflex_Distance_1_Left 0.1122420 0.1714674 -0.1216564 0.1380664
Marginal_Reflex_Distance_2_Right 0.1104928 0.1782834 -0.1824594 0.0254329
Marginal_Reflex_Distance_2_Left 0.1277416 0.1192784 -0.1826827 0.0252508
Philtrum -0.0099248 0.9040557 -0.3024779 0.0001685

IQ

There were no significant relationships with IQ.

PC1 p PC2 p
VIQ 0.0121797 0.9168325 -0.1635336 0.1580832
PIQ -0.0371284 0.7501639 -0.1593620 0.1691110
FSIQ 0.2568222 0.0251218 -0.0248672 0.8311500

Height

There were no significant relationships with height.

PC1 p PC2 p
PC1 -0.0813003 0.5746136 -0.0198157 0.8913569

Weight

There were no significant relationships with weight.

PC1 p PC2 p
PC1 -0.0843791 0.3807962 -0.0059876 0.9504979

GAF

There were no significant relationships with GAF.

PC1 p PC2 p
GAF_C 0.0454516 0.6104405 -0.1377357 0.1210422
GAF_H 0.0346279 0.7014400 -0.1545339 0.0852986

MMSE

There were no significant relationships with MMSE.

PC1 p PC2 p
PC1 0.0536625 0.5759074 -0.235307 0.0129165

Pathology

PS=Yes (M = 0.06, SD = 0.26) and PS=No (M = 0.06, SD = 0.26), t(150.00) = 0.00, p > .999, d < 0.01

2. Testing for differences in 22q-like factor scores between PS and NC

For the Penn sample (i.e. PS and NC groups), 2 factor scores are computed using the loadings above as weights in a linear combination of relevant gestalt scores (i.e. those gave hits in the 22q sample).

Note: any reference to PCs/factor scores below refers to those computed for the Penn sample.

PC 1

No significant differences between groups.

## 
##  Welch Two Sample t-test
## 
## data:  scores_df$PC1[TD] and scores_df$PC1[PS]
## t = 1.1348, df = 113.37, p-value = 0.2589
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.02576447  0.09485651
## sample estimates:
## mean of TD mean of PS 
##  0.3494327  0.3148866

PC 2

PC2 shows a difference between NC and PS groups.

## 
##  Welch Two Sample t-test
## 
## data:  scores_df$PC2[TD] and scores_df$PC2[PS]
## t = -3.8214, df = 109.14, p-value = 0.000221
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.16188349 -0.05131179
## sample estimates:
## mean of TD mean of PS 
## -0.1274639 -0.0208663

SZ vs CR

We see a difference in variances on PC2.

PC2: T-test

## 
##  Welch Two Sample t-test
## 
## data:  scores_df$PC2[SZ] and scores_df$PC2[CR]
## t = 1.4145, df = 52.243, p-value = 0.1631
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.02140075  0.12368864
## sample estimates:
##   mean of SZ   mean of CR 
## -0.005058168 -0.056202110

PC2: Levene’s test

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value   Pr(>F)   
## group  1   9.244 0.003667 **
##       53                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Emotrics

For univariate t-tests, the only difference between groups was in Philtrum measurements:

  • Brow Height (R): PS (M = 22.9, SD = 4.31) and TD (M = 23.45, SD = 4.17), t(146) = -0.76, p = .450, d = -0.13
  • Brow Height (L): PS (M = 22.89, SD = 3.9) and TD (M = 23.6, SD = 4.06), t(146) = -1.05, p = .295, d = -0.18
  • Marginal Reflex Distance 1 (R): PS (M = 2.66, SD = 1.13) and TD (M = 3.41, SD = 0.89), t(146) = -4.51, p < .001, d = -0.77
  • Marginal Reflex Distance 1 (L): PS (M = 2.62, SD = 1.13) and TD (M = 3.35, SD = 0.89), t(146) = -4.34, p < .001, d = -0.74
  • Marginal Reflex Distance 2 (R): PS (M = 4.94, SD = 1.04) and TD (M = 5.15, SD = 0.88), t(146) = -1.33, p = .186, d = -0.23
  • Marginal Reflex Distance 2 (L): PS (M = 4.93, SD = 0.99) and TD (M = 5.09, SD = 0.82), t(146) = -1.08, p = .281, d = -0.18
  • Philtrum: PS (M = 19.38, SD = 3.64) and TD (M = 21.54, SD = 3.91), t(146) = -3.33, p = .001, d = -0.57

PS vs TD Height

Height between PS and TD not significantly different.

## 
##  Welch Two Sample t-test
## 
## data:  ps_td_height$height[ps_td_height$group == "TD"] and ps_td_height$height[ps_td_height$group == "PS"]
## t = -0.55997, df = 25.921, p-value = 0.5803
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.845972  2.199355
## sample estimates:
## mean of x mean of y 
##  66.07143  66.89474

PS vs TD Weight

Weight between PS and TD not significantly different.

## 
##  Welch Two Sample t-test
## 
## data:  ps_td_weight$weight[ps_td_weight$group == "TD"] and ps_td_weight$weight[ps_td_weight$group == "PS"]
## t = -0.63805, df = 58.33, p-value = 0.5259
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -26.93719  13.91420
## sample estimates:
## mean of x mean of y 
##  167.6552  174.1667

PS vs TD GAF_C

GAF_C between PS and TD are significantly different.

## 
##  Welch Two Sample t-test
## 
## data:  ps_td_gafc$GAF_C[ps_td_gafc$group == "TD"] and ps_td_gafc$GAF_C[ps_td_gafc$group == "PS"]
## t = 7.2342, df = 40.147, p-value = 8.685e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  18.26382 32.42284
## sample estimates:
## mean of x mean of y 
##  84.82609  59.48276

PS vs TD GAF_H

GAF_H between PS and TD are significantly different.

## 
##  Welch Two Sample t-test
## 
## data:  ps_td_gafh$GAF_H[ps_td_gafh$group == "TD"] and ps_td_gafh$GAF_H[ps_td_gafh$group == "PS"]
## t = 6.6866, df = 38.538, p-value = 6.125e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  16.43823 30.70420
## sample estimates:
## mean of x mean of y 
##  85.26087  61.68966

PS vs TD MMSE

MMSE between PS and TD are not significantly different.

## 
##  Welch Two Sample t-test
## 
## data:  ps_td_mmse$MMSE[ps_td_mmse$group == "TD"] and ps_td_mmse$MMSE[ps_td_mmse$group == "PS"]
## t = 1.9034, df = 47.01, p-value = 0.06313
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.09303817  3.36133086
## sample estimates:
## mean of x mean of y 
##  28.56098  26.92683

3. Predicting PS status based on 22q-like PC2

## 95% CI: 0.5678-0.7484 (DeLong)

SZ and CR

Focusing the prediction on PS subgroups, results are similar to predicting on the aggregated PS group.

Schizophrenia (SZ)

## 95% CI: 0.5671-0.7809 (DeLong)

Clinical Risk (CR)

## 95% CI: 0.5053-0.7395 (DeLong)

4. Predicting PS status based on adjusted PC2

Sex- and -age adjustment

Prediction strength for PS status drops slightly after adjustment. However, prediction of CR status becomes essentially random post-adjustment.

## 95% CI: 0.5033-0.6931 (DeLong)

Schizophrenia (SZ)

## 95% CI: 0.5373-0.7551 (DeLong)

Clinical Risk (CR)

## 95% CI: 0.383-0.6349 (DeLong)

Height adjustment

Prediction strength for PS status doesn’t really change after adjusting for height.

## 95% CI: 0.4633-0.845 (DeLong)

Schizophrenia (SZ)

Adjusting for height increases prediction of SZ.

## 95% CI: 0.4672-0.89 (DeLong)

Clinical Risk (CR)

## 95% CI: 0.205-0.9665 (DeLong)

Weight adjustment

Prediction strength for PS status drops slightly after adjusting for weight.

## 95% CI: 0.4624-0.7406 (DeLong)

Schizophrenia (SZ)

Adjusting for weight decreases prediction of SZ.

## 95% CI: 0.4807-0.8158 (DeLong)

Clinical Risk (CR)

## 95% CI: 0.3711-0.7151 (DeLong)

GAF_C adjustment

Prediction strength for PS status decreases after adjusting for GAF_C.

## 95% CI: 0.3259-0.6516 (DeLong)

Schizophrenia (SZ)

Adjusting for GAF_C decreases prediction of SZ.

## 95% CI: 0.3888-0.8431 (DeLong)

Clinical Risk (CR)

Adjusting for GAF_C increases prediction of CR.

## 95% CI: 0.3786-0.7467 (DeLong)

GAF_H adjustment

Prediction strength for PS status decreases after adjusting for GAF_H.

## 95% CI: 0.32-0.6455 (DeLong)

Schizophrenia (SZ)

Adjusting for GAF_H decreases prediction of SZ.

## 95% CI: 0.4068-0.8541 (DeLong)

Clinical Risk (CR)

Adjusting for GAF_H increases prediction of CR.

## 95% CI: 0.3786-0.7467 (DeLong)

MMSE adjustment

Prediction strength for PS status decreases after adjusting for MMSe.

## 95% CI: 0.4639-0.714 (DeLong)

Schizophrenia (SZ)

Adjusting for MMSE decreases prediction of SZ.

## 95% CI: 0.4949-0.7601 (DeLong)

Clinical Risk (CR)

Adjusting for MMSE increases prediction of CR.

## 95% CI: 0.3895-0.7507 (DeLong)

Emotrics-adjusted scores

After adjusting PC2 for emotrics, prediction of SZ-status remains strong while CR becomes random.

## 95% CI: 0.5128-0.7028 (DeLong)

Schizophrenia (SZ)

## 95% CI: 0.5537-0.7717 (DeLong)

Clinical Risk (CR)

## 95% CI: 0.389-0.6408 (DeLong)

Conclusion